refac some remaining o3 statements by target var from datastore

src/run_modules/post_processing.py

@@ -34,6 +34,7 @@ class PostProcessing(RunEnvironment):
         self.train_data: DataGenerator = self.data_store.get("generator", "general.train")
         self.train_val_data: DataGenerator = self.data_store.get("generator", "general.train_val")
         self.plot_path: str = self.data_store.get("plot_path", "general")
+        self.target_var = self.data_store.get("target_var", "general")
         self.skill_scores = None
         self._run()
 
@@ -63,14 +64,13 @@ class PostProcessing(RunEnvironment):
     def plot(self):
         logging.debug("Run plotting routines...")
         path = self.data_store.get("forecast_path", "general")
-        target_var = self.data_store.get("target_var", "general")
 
         plot_conditional_quantiles(self.test_data.stations, pred_name="CNN", ref_name="orig",
                                    forecast_path=path, plot_name_affix="cali-ref", plot_folder=self.plot_path)
         plot_conditional_quantiles(self.test_data.stations, pred_name="orig", ref_name="CNN",
                                    forecast_path=path, plot_name_affix="like-bas", plot_folder=self.plot_path)
         PlotStationMap(generators={'b': self.test_data}, plot_folder=self.plot_path)
-        PlotMonthlySummary(self.test_data.stations, path, r"forecasts_%s_test.nc", target_var,
+        PlotMonthlySummary(self.test_data.stations, path, r"forecasts_%s_test.nc", self.target_var,
                            plot_folder=self.plot_path)
         PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, model_setup="CNN")
         PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, score_only=False,
@@ -102,7 +102,7 @@ class PostProcessing(RunEnvironment):
             input_data = self.test_data[i][0]
 
             # get scaling parameters
-            mean, std, transformation_method = data.get_transformation_information(variable='o3')
+            mean, std, transformation_method = data.get_transformation_information(variable=self.target_var)
 
             # nn forecast
             nn_prediction = self._create_nn_forecast(input_data, nn_prediction, mean, std, transformation_method)
@@ -147,7 +147,7 @@ class PostProcessing(RunEnvironment):
         return ols_prediction
 
     def _create_persistence_forecast(self, input_data, persistence_prediction, mean, std, transformation_method):
-        tmp_persi = input_data.sel({'window': 0, 'variables': 'o3'})
+        tmp_persi = input_data.sel({'window': 0, 'variables': self.target_var})
         tmp_persi = statistics.apply_inverse_transformation(tmp_persi, mean, std, transformation_method)
         window_lead_time = self.data_store.get("window_lead_time", "general")
         persistence_prediction.values = np.expand_dims(np.tile(tmp_persi.squeeze('Stations'), (window_lead_time, 1)),
@@ -227,7 +227,7 @@ class PostProcessing(RunEnvironment):
     def _get_external_data(self, station):
         try:
             data = self.train_val_data.get_data_generator(station)
-            mean, std, transformation_method = data.get_transformation_information(variable='o3')
+            mean, std, transformation_method = data.get_transformation_information(variable=self.target_var)
             external_data = self._create_orig_forecast(data, None, mean, std, transformation_method)
             external_data = external_data.squeeze("Stations").sel(window=1).drop(["window", "Stations", "variables"])
             return external_data.rename({'datetime': 'index'})